A fault prediction method for catenary of high-speed rails based on meteorological conditions

Fault frequency of catenary is related to meteo-rological conditions. In this work, based on the historical data, catenary fault frequency and weather-related fault rate are introduced to analyse the correlation between catenary faults and meteorological conditions, and further the effect of meteorological conditions on catenary oper-ation. Moreover, machine learning is used for catenary fault prediction. As with the single decision tree, only a small number of training samples can be classified cor-rectly by each weak classifier, the AdaBoost algorithm is adopted to adjust the weights of misclassified samples and weak classifiers, and train multiple weak classifiers. Finally, the weak classifiers are combined to construct a strong classifier, with which the final prediction result is obtained. In order to validate the prediction method, an example is provided based on the historical data from a railway bureau of China. The result shows that the mapping relation between meteorological conditions and catenary faults can be established accurately by AdaBoost algorithm. The AdaBoost algorithm can accurately predict a catenary fault if the meteorological conditions are provided.

基于运行工况和多分类支持向量机的柴油机共轨系统诊断方法

对于柴油机共轨系统计量阀复位弹簧松弛和喷油器针阀偶件磨损两类典型故障,提出了基于运行工况和多分类支持向量机的故障诊断策略.该策略考虑到运行工况对故障诊断精度的影响,根据车速将实车采集数据划分为3个子工况集.通过卡方检验和机理分析选择和故障情况相关度较高的状态参数,并使用主成分分析法提取特征参数,并根据轮廓系数筛选出对故障敏感度最高的子工况集.采用层次定比采样方法划分训练集,通过粒子群算法优化支持向量机的惩罚参数和径向基函数参数.最后通过实车测试数据集对模型进行验证,实验结果表明该方法在在突出工况的诊断正确率基本达到90%以上,满足故障诊断要求.

基于选择性集成学习的高速列车故障识别研究

在SVM分类识别中,分类器模型一经训练得到,对所有测试样本进行无差别的识别。针对高速列车故障中样本的分类识别是存在区域分类精度的情况,提出了一种基于选择性集成学习的SVM多分类器融合算法。该方法选取测试样本最邻近的k个训练样本;然后选择对其分类效果好的SVM分类器进行融合,以提高分类准确率;最后使用高速列车故障数据进行了实验,并与Ada Boost、KNN、Bayes、SVM分类方法进行了比较。实验结果表明,该算法提高了分类识别准确率。

城市轨道用地铁打磨车打磨控制系统设计

文章在介绍打磨车的基础上,设计了地铁用打磨控制系统,详细分析了打磨控制系统各部分的功能及实现方法。该系统采用成熟的PLC作为核心,应用了以太网、CAN总线等多种总线技术。